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Abstract

This paper proposes an algorithm to register OCT fundus images (OFIs) with color fundus photographs (CFPs). This makes it possible to correlate retinal features across the different imaging modalities. Blood vessel ridges are taken as features for registration. A specially defined distance, incorporating information of normal direction of blood vessel ridge pixels, is designed to calculate the distance between each pair of pixels to be matched in the pair image. Based on this distance a similarity function between the pair image is defined. Brute force search is used for a coarse registration and then an Iterative Closest Point (ICP) algorithm for a more accurate registration. The registration algorithm was tested on a sample set containing images of both normal eyes and eyes with pathologies. Three transformation models (similarity, affine and quadratic models) were tested on all image pairs respectively. The experimental results showed that the registration algorithm worked well. The average root mean square errors for the affine model are 31 µm (normal) and 59 µm (eyes with disease). The proposed algorithm can be easily adapted to registration for other modality retinal images.

Figures (6)

(a) The reference image IR (CFP) and the blood vessel ridge image (Ridge_ImageR) superimposed as black skeletons. (b) The target image IT (OFI) and its blood vessel ridge image (Ridge_ImageT) superimposed as red skeletons. (c) Registration result between the two blood vessel ridge images. (d) Registration result between the two original intensity images.

Given an investigated pixel on Ridge_ImageR, its matching pixel on Ridge_ImageT is the pixel closest to it. The Euclidian distance x with the penalty from normal direction difference θ, produces the modified distance y, which is used to classify the pair of matching pixels as a “successfully matched pair” (when y is less than the threshold thy) or a “non-successfully matched pair” (when y is greater than the threshold thy).

Estimation of translation parameters by similarity maps, where the x and y axes correspond to the translation differences along these axes. The side bars display the color map of similarities. (a) S1 similarity map, where the peripheral bright pixels are spurious peak similarities. (b) S similarity map with the correction term introduced, where the brightest pixel around at the center corresponds to the desired translation.

Examples of registration results. (a) Registration results for a normal eye by three transformation models. (b) Registration results for an eye with pathology. Registration errors in microns are displayed along with each figure.